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If the sample consisted of n observations, then the degrees of freedom is (n-1).

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Q: How do you find the degrees of freedom when using the t distribution to estimate or test the mean of a sample from a single population?
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You are using the t distribution to estimate or test the mean of a sample from a single population If the sample size is 25 then the degrees of freedom are?

There are 24 df.


How to calculate the degrees of freedom for t-distribution?

n-1


Does the t distribution always has n degrees of freedom?

Yes it does.


Is the shape of the chi-square distribution bsed on the degrees of freedom?

Yes.


Does t-distribution rely on degree of freedom?

Yes. The parameters of the t distribution are mean, variance and the degree of freedom. The degree of freedom is equal to n-1, where n is the sample size. As a rule of thumb, above a sample size of 100, the degrees of freedom will be insignificant and can be ignored, by using the normal distribution. Some textbooks state that above 30, the degrees of freedom can be ignored.


What does the student's t-distribution refer to in statistics?

The Student's T- Distribution is a type of probability distribution that is theoretical and resembles a normal distribution. The Student T- Distribution differs from the normal distribution by its degrees of freedom.


When do you know when to use t-distribution opposed to the z-distribution?

z- statistics is applied under two conditions: 1. when the population standard deviation is known. 2. when the sample size is large. In the absence of the parameter sigma when we use its estimate s, the distribution of z remains no longer normal but changes to t distribution. this modification depends on the degrees of freedom available for the estimation of sigma or standard deviation. hope this will help u.... mona upreti.. :)


Does Chi-square distribution becomes more skewed as the degrees of freedom increase?

No- skewness parameter declines with increased degrees of freedom. skewness = sqrt(8/k) see link


What does when the sample size and degrees of freedom is sufficiently large the difference between a t distribution and the normal distribution becomes negligible mean?

The t-distribution and the normal distribution are not exactly the same. The t-distribution is approximately normal, but since the sample size is so small, it is not exact. But n increases (sample size), degrees of freedom also increase (remember, df = n - 1) and the distribution of t becomes closer and closer to a normal distribution. Check out this picture for a visual explanation: http://www.uwsp.edu/PSYCH/stat/10/Image87.gif


A researcher collected 15 data points that seem to be reasonably bell shaped Which distribution should the researcher use to calculate confidence intervals?

A t-distribution with 15 degrees of freedom


What happens to the shape of the chi-square distribution as the df value increases?

As the value of k, the degrees of freedom increases, the (chisq - k)/sqrt(2k) approaches the standard normal distribution.


What is Chi-square Probability Distribution?

The Chi-square probability distribution is a probability distribution that describes the distribution of the sum of squared standard normal random variables. It is often used in hypothesis testing and is characterized by its degrees of freedom. The shape of the distribution depends on the degrees of freedom parameter, with larger degrees of freedom resulting in a more symmetric and bell-shaped distribution.